Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DistNet2D, a new deep neural network (DNN) architecture for 2D cell segmentation and tracking that leverages both mid- and long-term temporal information. DistNet2D considers seven frames at the input and uses a post-processing procedure that exploits information from the entire video to correct segmentation errors. DistNet2D outperforms two recent methods on two experimental datasets, one containing densely packed bacterial cells and the other containing eukaryotic cells. It is integrated into an ImageJ-based graphical user interface for 2D data visualization, curation, and training. Finally, we demonstrate the performance of DistNet2D on correlating the size and shape of cells with their transport properties over large statistics, for both bacterial and eukaryotic cells.

翻译：从显微视频中提取长时程细胞轨迹和谱系需要极低的错误率，而在密集或变形细胞的复杂数据集上实现这一目标极具挑战性。利用时间上下文信息是克服这一难题的关键。我们提出DistNet2D——一种用于二维细胞分割与追踪的新型深度神经网络架构，该网络能够同时利用中期和长期时间信息。DistNet2D在输入时考虑七帧图像，并通过后处理流程利用整个视频的信息修正分割错误。在两个实验数据集（包含密集排列的细菌细胞与真核细胞）上的实验表明，DistNet2D的性能优于两种近期方法。该网络已集成至基于ImageJ的图形用户界面中，支持二维数据可视化、数据修正及模型训练。最后，我们通过大规模统计验证了DistNet2D在关联细胞尺寸/形状与迁移特性方面的性能，研究对象覆盖细菌与真核细胞。